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It was popular in the early days of computing, but applies even more today, when powerful computers can produce large amounts of erroneous data or information in a short time. The first use of the term has been dated to a November 10, 1957, syndicated newspaper article about US Army mathematicians and their work with early computers,[1] in which an Army Specialist named William D. Mellin explained that computers cannot think for themselves, and that "sloppily programmed" inputs inevitably lead to incorrect outputs. The underlying principle was noted by the inventor of the first programmable computing device design:

On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

"Garbage in, gospel out" is a more recent expansion of the acronym. It is a sardonic comment on the tendency to put excessive trust in "computerised" data, and on the propensity for individuals to blindly accept what the computer says. Since the data entered into the computer is then processed by the computer, people who do not understand the processes in question, tend to believe the data they see:

Decision-makers increasingly face computer-generated information and analyses that could be collected and analyzed in no other way. Precisely for that reason, going behind that output is out of the question, even if one has good cause to be suspicious. In short, the computer analysis becomes a credible references point although based on poor data.[5]

The term can also be used as an explanation for the poor quality of a digitized audio or video file. Although digitizing can be the first step in cleaning up a signal, it does not, by itself, improve the quality. Defects in the original analog signal will be faithfully recorded, but may be identified and removed by a subsequent step by digital signal processing.

GIGO is commonly used to describe failures in human decision-making due to faulty, incomplete, or imprecise data. This sort of issue predates the computer age, but the term can still be applied.